Automatic accident detection

ABSTRACT

A method, apparatus and product for automatic accident detection. The method comprising: obtaining readings from a mobile device of a user carried thereby and not affixed to a vehicle in which the user is riding; determining, based on the readings obtained from the mobile device, that the user is riding in the vehicle; obtaining data from the mobile device of the user; and determining automatically, based on the data obtained from the mobile device, that the vehicle was involved in an accident.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/614,016 filed Jan. 5, 2018, entitled “Automatic Accident andCollision Detection”, and U.S. Provisional Application No. 62/716,125filed Aug. 8, 2018, entitled “Automatic Accident and CollisionDetection”, which are hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to automatic accident and collisiondetection in general, and to automatic accident and collision detectionbased on mobile devices input, in particular.

BACKGROUND

Accidents or traffic collisions are in a continuous growth due to therapid growth of technology and infrastructure that has increased thetraffic hazards and the distractors for drivers in the road. Accidentstake place more frequently which causes huge loss of life and property.Once an accident occurs, it may be desired to reach the accident as fastas possible to provide rescue facilities.

The faster that an accident is detected and reported, the faster theresponse from emergency services may be provided, in place to saveinjured persons, and to handle the damages caused to property.

BRIEF SUMMARY

One exemplary embodiment of the disclosed subject matter is a methodcomprising: obtaining readings from a mobile device of a user, whereinthe mobile device is being carried by the user, wherein the user isriding in a vehicle, wherein the mobile device is not affixed to thevehicle; determining, based on the readings obtained from the mobiledevice, that the user is riding in the vehicle; obtaining data from themobile device of the user; and determining automatically, based on thedata obtained from the mobile device, that the vehicle was involved inan accident.

Another exemplary embodiment of the disclosed subject matter is anon-transitory computer readable medium retaining program instructions,which program instructions when read by a processor, cause the processorto perform: obtaining readings from a mobile device of a user, whereinthe mobile device is being carried by the user, wherein the user isriding in a vehicle, wherein the mobile device is not affixed to thevehicle; determining, based on the readings obtained from the mobiledevice, that the user is riding in the vehicle; obtaining data from themobile device of the user; and determining automatically, based on thedata obtained from the mobile device, that the vehicle was involved inan accident.

Yet another exemplary embodiment of the disclosed subject matter is anapparatus comprising: a processor and a memory, wherein the processor isconfigured to perform: obtaining readings from a mobile device of auser, wherein the mobile device is being carried by the user, whereinthe user is riding in a vehicle, wherein the mobile device is notaffixed to the vehicle; determining, based on the readings obtained fromthe mobile device, that the user is riding in the vehicle; obtainingdata from the mobile device of the user; and determining automatically,based on the data obtained from the mobile device, that the vehicle wasinvolved in an accident.

Optionally, said obtaining the data comprises obtaining a call log ofthe mobile device; wherein said determining automatically that thevehicle was involved in an accident comprises detecting a call patternin the call log indicative of an occurrence of an accident.

Optionally, the call pattern is selected from the group consisting of:an outgoing call to a phone number of emergency services; an outgoingcall to a phone number of an insurance company; a plurality of repeatedcalls to a phone number in a frequency higher than an average callattempt frequency of the user; and a plurality of outgoing calls andcorresponding incoming calls to and from family members of the user,wherein a duration of the plurality of outgoing calls and correspondingincoming calls is below an average call duration of the user.

Optionally, identifying a phone call performed using the mobile devicethat was terminated abnormally, and determining a time of the accidentbased on a time of a termination of the phone call.

Optionally, said identifying the phone call is performed using amicrophone of the mobile device and based on a recording of a lastperiod of the phone call.

Optionally, said identifying the phone call is performed based on a calllog of the mobile device, wherein said identifying comprises identifyinga phone call with a participant having a duration below an average callduration of the user with respect to the participant.

Optionally, the mobile device comprising a sensor capable of sensingmotion; wherein said determining automatically that the vehicle wasinvolved in the accident comprises identifying a driving pattern of thevehicle that is consistent with a post-accident driving pattern.

Optionally, the mobile device comprising a sensor capable of sensingmotion; wherein said determining automatically that the vehicle wasinvolved in the accident comprises identifying a driving pattern of thevehicle in a road segment that deviates from an average driving patternof vehicles in the road segment.

Optionally, the mobile device comprising a sensor capable of sensingmotion; wherein said determining automatically that the vehicle wasinvolved in an accident comprises: identifying a mobility state of theuser, based on readings of the sensor, indicative of the user not ridingthe vehicle.

Optionally, the mobile device comprising a sensor capable of sensingmotion; wherein said determining automatically that the vehicle wasinvolved in an accident comprises: identifying a mobility pattern of theuser comprising an ordinal sequence of mobility states of the user,wherein the mobility pattern is indicative of the user being involvedwith the accident.

Optionally, the mobility pattern comprises at least a sequence of afirst mobility state indicative of the user riding a vehicle, a secondmobility state, immediately following the first mobility state,indicative of a sudden stop; and a third mobility state occurring afterthe second mobility state, indicative of a user walking.

Optionally, said determining automatically that the vehicle was involvedin an accident is performed at least a predetermined time after theaccident, wherein said determining is performed based on a lack ofmatching between scheduled activities of the user, as appearing in acalendar of the user that is retained in the mobile device, and actualactivities of the user.

Optionally, the data obtained from the mobile device is one or morepictures taken by the mobile device, wherein said determiningautomatically that the vehicle was involved in an accident is performedbased on identifying in the one or more pictures a damaged vehicle orportion thereof.

Optionally, automatically determining, based on the one or morepictures, an estimated severity level of the accident.

Optionally, automatically identifying in the one or more pictures, alicense plate of a vehicle, and determining one or more involvedvehicles in the accident.

Optionally, the mobile device comprising a microphone, wherein saidobtaining data comprises obtaining a recording of the microphone,wherein said determining automatically that the vehicle was involved inan accident comprises analyzing the recording to identify an audiosegment indicative of the accident.

Optionally, the mobile device retaining a plurality of applications,wherein said obtaining data comprises obtaining a usage information ofthe plurality of applications, wherein said determining automaticallythat the vehicle was involved in an accident comprises analyzing theusage information of the plurality of applications.

Optionally, in response to said determining automatically that thevehicle was involved in an accident, automatically reporting theaccident to a third party.

Optionally, in response to said determining automatically that thevehicle was involved in an accident, automatically updating a calendarof the user, wherein the calendar is retained in the mobile device.

Optionally, said determining automatically that the vehicle was involvedin the accident comprises extracting a feature vector from the data,wherein the feature vector is associated with a sliding window withrespect to a sensor of the mobile device.

Optionally, the mobile device comprising an accelerometer, wherein saidobtaining the readings comprises obtaining accelerometer readings fromthe accelerometer, wherein said determining that the user is riding inthe vehicle comprises analyzing the accelerometer readings of the mobiledevice to determine mobility state of the user.

Optionally, obtaining data from a computing device of the vehicle;wherein said determining automatically that the vehicle was involved inan accident, is further determined based on the data obtained from thecomputing device.

Optionally, performing an authentication of said determiningautomatically that the vehicle was involved in an accident, based on thedata obtained from the computing device.

Optionally, the data obtained from the computing device of the vehiclecomprises one or more pictures captured by a dash camera of the vehicle;wherein said determining automatically that the vehicle was involved inan accident is performed based on analysis of the one or more pictures.

THE BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosed subject matter will be understood and appreciatedmore fully from the following detailed description taken in conjunctionwith the drawings in which corresponding or like numerals or charactersindicate corresponding or like components. Unless indicated otherwise,the drawings provide exemplary embodiments or aspects of the disclosureand do not limit the scope of the disclosure. In the drawings:

FIG. 1 shows a schematic illustration of an exemplary vehicle accident,in accordance with some exemplary embodiments of the disclosed subjectmatter;

FIGS. 2A-2D show schematic illustrations of exemplary movement patternsof a vehicle involved in an accident, in accordance with some exemplaryembodiments of the disclosed subject matter;

FIGS. 3A-3H show flowchart diagrams of a method, in accordance with someexemplary embodiments of the disclosed subject matter; and

FIG. 4 shows a block diagram of an apparatus, in accordance with someexemplary embodiments of the disclosed subject matter.

DETAILED DESCRIPTION

One technical problem dealt with by the disclosed subject matter is toautomatically detect that a vehicle is involved with an accident. Thehigh demand of automobiles has also increased the traffic hazards androad accidents. The faster an accident is detected and reported, thebetter it may be handled. As an example, an effective automatic accidentdetection and information provider may be required in place to saveinjured persons, by allowing faster response from emergency services.

Additionally or alternatively, an automatic vehicle accident detectionmay provide wider and more efficient information regarding the accidentthat may be required for both of the emergency services and to usersaffected directly or indirectly from the accident, such as geographicalcoordinates, time and angle in which a vehicle accident had occurred,people associated with the accident, or the like. On the one hand, alertmessages may be sent to rescue teams in a short time, which will help insaving the valuable lives. On the other hand, alert messages may be sentto relevant people associated with people involved with the accident,that may not usually be alerted of such accidents, such as people thattheir schedule may be affected by the accident, family, friends,insurance agents, or the like.

One technical solution is to automatically determine, based on dataobtained from mobile devices carried by users riding a vehicle, that thevehicle was involved in an accident. In some exemplary embodiments,existing computing capabilities, sensors and tools of such computingdevices, may be utilized to determine that the vehicle is involved in anaccident. As most people today, carry computing devices such as smartphones or other portables devices, the solution may be to automaticallydetect that a vehicle is involved in an accident based on data providedfrom computing devices of users associated with the vehicle, such as adriver, passengers, drivers of adjacent vehicles, or the like.

Another technical solution is to determine that the vehicle is involvedin an accident, based on a behavior pattern of users of the mobiledevice associated with the vehicle, with respect to their mobiledevices. In some exemplary embodiments, when a vehicle is involved in anaccident, drivers, passengers, pedestrians or the like, involved in theaccident, may tend to behave in a common behavior. Additionally oralternatively, the behavior of other drivers in the area of theaccident, with respect to their computing device, may be indicative ofthe accident.

In some exemplary embodiments, the data may be obtained from a mobiledevice carried by a user, while the user is riding in the vehicle. Itmay be appreciated that the mobile device may not be affixed to thevehicle.

In some exemplary embodiments, prior to obtaining the data from themobile device to determine involvement in an accident, a determinationwhether the user of the mobile device is riding the vehicle may beperformed based on readings obtained from the mobile device. As anexample, a mobility state of the user may be determined based onreadings of an accelerometer of the computing device. Thus, if the useris riding a vehicle, the speed of the associated mobile device may bethat of the vehicle. However, if the mobile device is with the user ass/he walks either to or from the car, the speed may be slower.

In some exemplary embodiments, when a small accident occurs, the drivermay stop the vehicle, then drive or move it to the side of the road. Thedriver or the passengers of the vehicle may go outside the car and takea picture of the damage using their smartphones, mobile device cameras,or the like. The driver or the passengers of the vehicle may performphone calls to relatives, an insurance company, to the police, tomedical help, to any other service provider, or the like. Such behaviorsmay be indicative that an accident has occurred.

In some exemplary embodiments, the data obtained from the mobile deviceof the user may be obtained from a sensor of the mobile device that iscapable of sensing motion. The data obtained from such sensor may beanalyzed to determine the pattern of motion that the mobile device isbeing carried on, such as the pattern of driving of the vehicle that theuser holding the mobile device is riding, a mobility state of the user,a mobility pattern of the user, or the like. The system may beconfigured to determine automatically that the vehicle was involved inthe accident, based on identifying a driving pattern of the vehicle thatis consistent with a post-accident driving pattern. Additionally oralternatively, the system may be configured to determine automaticallythat the vehicle was involved in the accident, based on identifying adriving pattern of the vehicle in a road segment that deviates from anaverage driving pattern of vehicles in the road segment.

In some exemplary embodiments, a driving pattern of vehicles in the areaof the accident may be indicative thereof. As an example, when anaccident occurs, vehicles driven in the area of the accident may stop orslow down to see what happened, then return to their normal drivinghabits. As another example, a congestion of traffic may occur at thatarea, indicating that an accident has occurred. As yet another examples,such vehicles may slow down and veer to the sideway of the street, tovacate the road to the emergency services vehicles. Additionally oralternatively, users associated with the accident, such the driver,passenger of the vehicle, or the like, may have a common mobilitybehaviour pattern consequent upon the accident. As an example, the usersmay be in a driving mode before the accident occurs, then they may stopand walk outside the vehicle to check out the damage, or the like.

Additionally or alternatively, the driving pattern of a vehicle beingdifferent than the driving pattern of other vehicles in the same area orthe same route, may be indicative of the vehicle being involved with anaccident. As an example, a vehicle involved in an accident may stop in aroad while other vehicles are driving. As another example, a vehicleinvolved in an accident may stop and cause a traffic congestion behindthereof.

In some exemplary embodiments, a mobility state of the user of themobile device may be determined based on readings of one or more motionsensors. The mobility state may be checked to determine if it isindicative of the user not riding the vehicle. Such may be an unexpectedindication of being involved in an accident. If the user is not ridingthe vehicle, after being riding vehicle based on the readings of themobile device, this may be indicative that the user had to get off thecar suddenly, such as to check the vehicle, the exchange informationwith the second party, or the like.

Additionally or alternatively, a mobility pattern of the user may beidentified based on the data obtained from the motion sensors. Themobility pattern may comprise an ordinal sequence of mobility states ofthe user. The mobility pattern may be analyzed to determine if it isindicative of the user being involved with the accident. As an example,a mobility pattern comprising a sequence of a first mobility stateindicative of the user riding a vehicle, a second mobility state,immediately following the first mobility state, indicative of a suddenstop; and a third mobility state occurring after the second mobilitystate, indicative of a user walking; may be indicative of the vehiclebeing involved in an accident. It may be appreciated, that the drivingpattern of the vehicles, the mobility pattern of the users, or the like,may be automatically determined by analyzing, in real time, the speedand or locations of vehicles on the location, based on data obtainedfrom computing devices associated with the vehicles.

Additionally or alternatively, the mobility pattern may be automaticallydetermined based on data obtained from other sensors of such mobiledevices. As an example, indicating that the user is leaving the vehiclemay be performed based determining that the user is walking. Determiningthat the user is walking may be performed based on data retrieved fromthe accelerometer of the computing device. As another example,indicating that the user is leaving the vehicle may be performed basedon detecting that the charger of the mobile device has beendisconnected, by detecting that the signal strength of the Bluetooth orother wireless connection to a device in the vehicle (e.g., wireless orBluetooth device in the car) weakens, or the like. As yet anotherexample, using the barometer readings of the device it is possible to“feel” the barometric pressure change between the car and the outsideenvironment and thus understanding that the device or the user is insidethe vehicle or went outside the vehicle.

In some exemplary embodiments, users associated with the accident, suchthe driver, passenger of the vehicle, or the like, may have a commonphone call behaviour pattern consequent upon the accident. As anexample, the users may stop a call due to the accident, may performcalls to emergency services, may perform calls to certain family memberstelling that they have been involved in an accident, or the like. Insome exemplary embodiments, the data obtained from the computing devicemay comprise a call log of the mobile device. The call log may beanalyzed to determine an indication of an occurrence of an accident,such as an outgoing call to a phone number of emergency services, anoutgoing call to a phone number of an insurance company, a plurality ofrepeated calls to a phone number in a frequency higher than an averagecall attempt frequency of the user, a plurality of outgoing calls andcorresponding incoming calls to and from family members of the user witha duration below an average call duration of the user, or the like.

In some exemplary embodiments, a phone call performed using the mobiledevice that was terminated abnormally may be identified, such as basedon a recording of a last period of the phone call, based on an inputfrom a microphone of the mobile device, based on the call log comprisinga phone call with a participant having a duration below an average callduration of the user with respect to the participant, or the like. Theinformation about the abnormally terminated phone call, such as thetermination time, duration, or the like, may be analyzed to determininga time of the accident.

In some exemplary embodiments, after an accident occurs, the user mayattempt to call a destination, and may repeat the call in a highfrequency. In some cases, the call frequency to the same destination maybe higher than an average call-attempt frequency of the user, eithergenerally to all number, specifically to the second participant of thephone calls, or the like. Additionally or alternatively, the callfrequency may be in a relatively high percentile of the call-attemptfrequency, such as above 75%, above 80%, or the like. Additionally oralternatively, the calls may be relatively short, such as below anabsolute threshold, below a relative threshold (e.g., 10% percentile ofthe user), or the like. Additionally or alternatively, the destinationmay call back to the user in multiple attempts. Consider a mother whoreceives a call from her son notifying her he was involved in anaccident. The mother may call her son over and over in a relatively highfrequency (e.g., above her regular frequency, above her regularfrequency as observed in the user's call log, in a relatively highpercentile of her call back frequency (e.g., 75%, 80%, 90%, or thelike).

In some exemplary embodiments, when a driver is involved in an accident,such an event may disrupt the driver's overall regular timeline. Eventsshe is involved with in may change with respect to the regular schedule.As an example, the driver may cancel or miss calendar events, may arrivelater to her next destination, may arrive home after work later thanusual, or the like. Such changes may be indicative of an unexpectedevent such as an accident. In some exemplary embodiments, thedetermination that the vehicle was involved in an accident may beperformed at least a predetermined time after the accident, such asafter few hours, at the end of the day, a day after, or the like. Thedetermination may be performed based on a lack of matching betweenscheduled activities of the user, as appearing in a calendar of the userthat is retained in the mobile device, and actual activities of theuser. The lack of matching may be indicative of a sudden event thatprevented the user from fulfilling her scheduled activities. Along withthe additional data obtained from the mobile device, indicating that theuser was riding the vehicle, the involvement in the accident, along withadditional information related thereto, may be determined.

In some exemplary embodiments, the behavior pattern may be automaticallydetermined based on data obtained from applications, backgroundservices, or the like, that are installed on computing devices of users,such as the driver of the vehicle, passengers on the vehicle, passengersof other vehicles in the area, pedestrians in the area, or the like. Thedriver or passengers of vehicles involved with the accident, may invokecertain apps indicative of the accident, such as an insurance app,browsing specific web pages, or the like.

In some exemplary embodiments, the data obtained from the mobile devicemay comprise one or more pictures taken by the mobile device. The one ormore pictures may be analyzed to determine an estimated severity levelof the accident. As an example, the images may be analyzed to identify adamaged vehicle or portion thereof, and determine the severity of theaccident based on the severity of the damage. Additionally oralternatively, additional data may be extracted from the pictures, suchas determining involved vehicles in the accident based on identifiedlicense plates of vehicles in the pictures, or the like.

Additionally or alternatively, apps or background services running oncomputing devices may be configured to detect that a camera of thecomputing device was turned on, switched, that a new photo was added tothe device memory, or the like. Recent or newly added photos may beanalyzed automatically to detect if a vehicle appears in the photo. Thephotos may be analyzed to automatically detect if the photo comprises avehicle, a part of a vehicle, a damaged part of a vehicle, or the like.Image analysis tools, object recognition tools, or the like, may beutilized to determine the type of damage caused to the vehicle, locationof damage, or the like. A picture classifier may be utilized todetermine automatically the type of the car, severity of accident, platenumber, or the like. Such data may be utilized in order to understand ifthere was an accident, how severe is the accident, associating betweenthe vehicle of the owner thereof (e.g., by identifying the plate number,type of the car, color, or the like), or the like. Additionally oralternatively, such classifier may be used to detect the severity anddetails of the other involved party in the accident, such as othervehicles, pedestrian, or the like. In some cases, information extractedautomatically based on the photos may be used to automatically fillaccident details in a report or digital record, such as records ofemergency services, records of insurance, or the like. It may beappreciated that different parameters of the behavior pattern may beautomatically determined based on data obtained from a camera of thecomputing device. As an example, it may be possible to see that othervehicles or objects are moving while the vehicle is not moving and thisway to understand that the car is stuck for some reason.

Additionally or alternatively, the behavior pattern may be automaticallydetermined based on data obtained from voice sensors of the mobiledevice. As an example, apps or background service may be configured todetect that a phone call was disconnected suddenly, that a shout or asiren has been heard through the microphone, that a call has been placedto relatives or emergency services or any other kind of service providerthat is related to such an event, or the like. As another example, usingthe microphone it may be possible to hear the vehicle hitting or gettinghit by another object, shouts of the driver or passenger, the vehicledoor opening closing, or the like.

In some exemplary embodiments, a car dash camera may be installed in thevehicle. In some cases, the car dash camera may record in relativelyshort intervals videos indicative of the road (e.g., front view, sideview, back view, or the like). In some cases, a mobile device mayfunction as a car dash camera or may be connectable thereto. The videosmay be reviewed to automatically detect the accident, identifyinformation relating to the accident, or the like. In some cases,information from the car dash camera may be reviewed in real-timeconsistently. Additionally or alternatively, video from the car dashcamera may be reviewed only after a potential accident event is detectedand used to validate the event or refute it. In such a case, computationand power resources are utilized in a more efficient manner andcomputational-intense processing is performed after initial indicationof existence of an event is detected.

In some exemplary embodiments, various sensor readings of the mobiledevice may be gathered and a feature vector may be extracted therefrom.The feature vector may represent sensor readings in a sliding windowwith respect to some of the sensors (e.g., last one minute ofaccelerometer readings). Additionally or alternatively, the featurevector may represent a true/false indication of an event, e.g., a loudsound was heard in the last 60 minutes, or the like. In some exemplaryembodiments, a reading of the same sensor may be provided to differenttime windows which may or may not overlap. As an example, accelerometerinformation may be provided for 60 windows, each of which of 10 seconds.

In some exemplary embodiments, a classifier may be utilized todetermine, based on the feature vector, that an accident has occurred.The classifier may be trained using training data. The classifier may bea global model generated based on behavior of many people or anindividual model of an individual person behavior. In some exemplaryembodiments, cluster of users may be compiled, and for each cluster amodel may be trained, thereby providing a model useful for “look-a-like”users that behave in a similar manner.

In some exemplary embodiments, the training data may be obtained usingdata on accidents from third party sources, such as incidentsinformation services like Waze™ or Google™ maps or information fromservice providers such as insurance companies or car manufacturers callcenters, or the like. The data may be used to obtain a label andcorrelate the label with a feature vector that is obtained from thethird party source or obtained using an agent installed on the device,or the like. As an example, an agent may monitor behavior of the userand the information about the accident may be obtained from the user'sinsurance company. Using such information, the behavior of the user atthe time of the accident may be obtained from the agent's log. In somecases, the precise timing may not be known. GPS and location informationmay be used to determine when the user was located at or near the placeof the accident, and anomaly detection may be used to identify thetimeframe in which the user acted abnormally to identify when exactlythe accident occurred.

In some exemplary embodiments, a confidence level parameter may beattached to the accident event. As presented above, the event ofaccident is a set of variety of indications from different sources.Therefore, the confidence level may be gradually increased if moreindication is accumulated until it reaches a predefine threshold. Amachine learning algorithm may be used to learn the correlation betweenany physical or virtual (such as web browsing, app launching, or thelike) indications, to the occurrence of an accident; in order toautomatically classify an accident event.

One technical effect of utilizing the disclosed subject matter is toenable a fast and effective response to road accidents. The disclosedsubject matter provides an automatic detection and report of accidents,while providing accurate information about the accidents to theemergency service, such as exact location, severity of the accident, thedamage caused by the accident, or the like. The detection and thenotification of the accidents are performed automatically using toolsexisting in mobile devices that are carried by everyone. Comparing withexisting accident detection and notification approaches, that may beexpensive, maintenance complex task, and not available in all vehicles,the disclosed subject matter utilizes the processing power andtechnology of mobile devices that are available to all drivers andpassengers.

Another technical effect of utilizing the disclosed subject matter is toprovide an automatic accident detection without requiring any specialactivity from the user. The disclosed subject matter enables detectionof the accident based on data from mobile device that may be obtainedand analyzed automatically without explicit input or action from theuser.

Yet, another technical effect of utilizing the disclosed subject matteris to enable an ex post facto detection of accidents. Small accidents,or low severity accidents may not always be required to be reported toemergency services or family members. However, such accident may berequired to be detected and reported, even after the accident occurs,such as to be reported to insurance services, to update schedules basedthere upon, or the like. The disclosed subject matter enables toautomatically determine that a vehicle was involved in an accident, apredetermined time after the accident occurred, such as after few hoursor a day or the like, even while not having the real time data of theaccident. After the accident being detected, additional informationabout the accident may be automatically and retroactively extractedbased on logs of the mobile device.

The disclosed subject matter may provide for one or more technicalimprovements over any pre-existing technique and any technique that haspreviously become routine or conventional in the art. Additionaltechnical problem, solution and effects may be apparent to a person ofordinary skill in the art in view of the present disclosure.

Referring now to FIG. 1 showing a schematic illustration of an exemplaryvehicle accident, in accordance with some exemplary embodiments of thedisclosed subject matter.

In some exemplary embodiments, FIG. 1 may show a schematic illustrationof an exemplary vehicle accident occurred between Vehicle 110 andVehicle 120. The vehicle accident may be automatically determined basedon data obtained from one or more mobile devices associated with thevehicles involved in the accident, such as Mobile Device 114 of a driverof Vehicle 110, Mobile Device 112 of a passenger in Vehicle 110, MobileDevice 122 of a driver of Vehicle 120, or the like. Additionally oralternatively, the vehicle accident may be automatically determinedbased on data obtained from one or more computing devices of thevehicles involved in the accident, such as Dash Camera 116, or the like.Additionally or alternatively, Vehicle 110 may be an autonomous car, andthe data may be provided by a computer system of Vehicle 110.

In some exemplary embodiments, Mobile Device 112 may be carried by auser, such as a passenger or a driver riding in Vehicle 110. MobileDevice 112 may be not be affixed to the vehicle. Mobile Device 112 maycomprise an accelerometer capable of sensing the acceleration of MobileDevice 112, other motion sensors such as speedometer, barometer, or thelike. Readings from Mobile Device 112 may be obtained. The readings maycomprise accelerometer readings from the accelerometer, readings fromthe other motions sensors, locations readings for apps of Mobile Device112, or the like. The readings may be analyzed to determine mobilitystate of the user. Based on mobility state of the user, the user may bedetermined to be riding in Vehicle 110. As an example, based on anacceleration reading of Mobile Device 112 indicating that the user is ina driving mobility state, the user may be determined to be riding inVehicle 110.

In some exemplary embodiments, data may be obtained from Mobile Device112, Mobile Device 114, or the like. The data may comprise a call log ofMobile Device 112 (or of each mobile device), data from a sensor capableof sensing motion of, audio recordings from a microphone of MobileDevice 112, pictures captured by Mobile Device 112, usage information ofapplications of Mobile Device 112, recordings of phone calls of MobileDevice 112 (e.g., from recordings apps), or the like. The data may beanalyzed to identify a motif, a pattern of behavior, or the like thatmay be indicative of the occurrence of the accident. As one example, thecall log data may be analyzed to detect a call pattern indicative of anoccurrence of an accident, such as an outgoing call from Mobile Device112 to a phone number of emergency services, an outgoing call fromMobile Device 112 to a phone number of an insurance company, a pluralityof repeated calls to a phone number in a frequency higher than anaverage call attempt frequency of the user of Mobile Device 112 to thisphone number, a plurality of repeated calls to a phone number in afrequency higher than an average call attempt frequency of the user ofMobile Device 112 to all numbers, a plurality of outgoing calls andcorresponding incoming calls to and from family members with a durationbelow an average call duration of the user of Mobile Device 112, or thelike. As another example, the call log data and the audio recordings maybe analyzed to identify a phone call performed using Mobile Device 112that was terminated abnormally, and determining a time of the accidentbased on a time of a termination of the phone call. As yet anotherexample, the usage information of applications of Mobile Device 112 maybe analyzed to determine if Vehicle 110 was involved in an accident,such as based on accessing insurance applications, publishing theaccident in social networks, or the like.

In some exemplary embodiments, motion data, such as data obtained fromsensors of Mobile Device 112 capable of sensing motion may be analyzedto determine a driving pattern of Vehicle 110. The driving pattern ofVehicle 110 may be analyzed to determine if being consistent with apost-accident driving pattern. Additionally or alternatively, thedriving pattern of Vehicle 110 in the road segment of the vehicle may becompared with the driving pattern of other vehicles in the same roadsegment. If the driving pattern of Vehicle 110 deviate from an averagedriving pattern of vehicles in the same road segment, and accident maybe indicated.

Additionally or alternatively, a mobility state of the user of MobileDevice 112 may be determined based on readings of the motion sensorsthereof. A mobility state indicative of the user not riding Vehicle 110,(e.g., as in Mobile Device 112′), after already indicating the user wasriding Vehicle 110 (e.g., as in Mobile Device 112), may be indicative ofan accident. The user may be expected to get off Vehicle 110, such as totake pictures of the accident, to exchange information with the driverof the other vehicle, or the like. Thus, a walking mobility state may beindicative of Vehicle 110 being involved in an accident. In someexemplary embodiments, the data may be analyzed to determine a mobilitypattern of the user of Mobile Device 112 (112′). The mobility patterncomprise an ordinal sequence of mobility states of the user. Somemobility patterns may be indicative of the user being involved with theaccident. As an example, a mobility pattern comprising a sequence of afirst mobility state indicative of the user riding a vehicle (e.g. basedon data from Mobile Device 112, indicating that the user is ridingVehicle 110); a second mobility state, immediately following the firstmobility state, indicative of a sudden stop (e.g. based on data fromMobile Device 112); and a third mobility state occurring after thesecond mobility state, indicative of a user walking (e.g. based on datafrom Mobile Device 112′), may be indicative of the user being involvedin the accident.

Additionally or alternatively, the driving pattern may be utilized todetermine the circumstances led to the accident, the vehicle responsibleof the accident, or the like. As an example, the driving pattern may beindicative of uncircumspect driving, driving in a higher speed, adeviation from the expected path, or the like.

In some exemplary embodiments, the obtained data may comprise one ormore pictures taken by Mobile Device 112′ (e.g., Mobile Device 112carried by the user after getting off Vehicle 110). The one or morepictures may be analyzed to detect a damaged portion of Vehicle 110 suchas Hit 110 a, a damaged portion of Vehicle 120 such as Hit 120 a, or thelike. Such motifs may be indicative of an accident. An estimatedseverity of the accident may be determined based on the pictures, suchas based on the scope of Hit 110 a and Hit 120 a, the severity thereof,or the like. Additionally or alternatively, a License Plate 130 ofVehicle 110 or License Plate 132 of Vehicle 120, may be detected in thepictures to determine the involved vehicles in the accident.Additionally or alternatively, the pictures may be provided asadditional information about the accident, such as for insurance issues,police reports, or the like.

In some exemplary embodiments, pictures and records of Dash Camera 116of Vehicle 110 may be analyzed. Dash Camera 116 may be configured recordvideos indicative of the road (e.g., front view, side view, back view,or the like). Dash Camera 116 may be configured record videos inrelatively short intervals, such as of 10 seconds, 20 seconds, or thelike. In some cases, Mobile Device 112 may be connectable to Dash Camera116, and the data provided by Dash Camera 116 may be obtainable fromMobile Device 112. Videos and pictures provided by Dash Camera 116 maybe reviewed to automatically detect the accident, identify informationrelating to the accident, or the like. In some cases, information fromDash Camera 116 may be reviewed in real-time consistently. Additionallyor alternatively, video from Dash Camera 116 may be reviewed only aftera potential accident event is detected and used to validate the event orrefute it. In such a case, computation and power resources are utilizedin a more efficient manner and computational-intense processing isperformed after initial indication of existence of an event is detected.

Referring now to FIGS. 2A-2D showing a schematic illustration of anexemplary movement pattern of a vehicle involved in an accident, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

In some exemplary embodiments, the driving pattern illustrated in FIGS.2A-2D may be indicative of an accident.

In FIG. 2A, Vehicle 200 may be driving straight towards an Obstacle 210.Obstacle 210 may be another vehicle, a stone, a wall, or the like. Themobility state determined based on mobile devices temporary associatedwith Vehicle 200 may indicate a state of driving forward. The mobilitystate may be determined based on accelerometer readings of the mobiledevices temporary associated with Vehicle 200, or based on readings ofother sensors thereof, such as speedometer readings, barometer readings,combination thereof, or the like.

In FIG. 2B, Vehicle 200 may hit Obstacle 210. As a result, the mobilitystate of users riding in Vehicle 200 may indicate a sudden stop.

In FIG. 2C, the readings may indicate that Vehicle 200 is drivingbackward. Such state may be determined based on negative accelerometerreadings (of opposite direction of the readings of FIG. 2A), based onreadings of a gyroscope of the mobile device, magnetometer readings,combination thereof, or the like.

In FIG. 2D, the readings obtained from the mobile device may indicatethat Vehicle 200 turned to the right, e.g., to stop at the side of theroad.

Referring now to FIG. 3A showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 310, readings from a mobile device of a user associated with avehicle may be obtained. In some exemplary embodiments, the mobiledevice may be temporary associated with the vehicle. The mobile devicemay be carried by the user while the user is riding in the vehicle.However, the mobile device may not affixed to the vehicle.

In some exemplary embodiments, the user may be a driver of the vehicle,a passenger in the vehicle, a passenger of an adjacent vehicle, or thelike.

In some exemplary embodiments, the mobile device may comprise anaccelerometer. The accelerometers may be configured to be used for userinterface control, such as for view adjustment, as pedometers, inconjunction with specialized applications, or the like. Theaccelerometer may be configured to measure proper acceleration of themobile device, e.g., the rate of change of velocity of the mobile devicein its own instantaneous rest frame. Readings of the accelerometer ofthe mobile device may be obtained.

On Step 320, a determination that the user is riding in the vehicle maybe performed based on the readings obtained from the mobile device.

In some exemplary embodiments, the accelerometer readings may beanalyzed to determine a mobility state of the user. The accelerometerreadings may vary according to the user's motion (i.e. walking, running,driving, riding a bike, sleeping, or the like). As an example, the usermay be determined to be riding in the vehicle based on the accelerometerreadings of the mobile device to determine mobility state of the user.

It may be appreciated that while most mobile devices are equipped with aGPS from which it is possible to determine the speed at which the deviceis moving, the GPS consumes much more power than the accelerometer.Accordingly, using accelerometer data may more be economical when it isnecessary to constantly monitor the mobility status and may be availablewhen the GPS is not.

In accordance with a preferred embodiment of the present invention, themobility status of a mobile device may be determined using thestatistical characteristics of the accelerometer readings. For example,a driving pattern may be classified and distinguished from otheractivities by detecting constant constraint vibrations occurring whiledriving compared to abrupt accelerations when walking or running or verylow acceleration when the mobile device is not moving.

On Step 330, data may be obtained from the mobile device of the user. Insome exemplary embodiments, the data may comprise variant types of data,such as data about apps consuming, call log data, photos, data from amicrophone of the mobile device, accelerometer readings, barometerreadings, GPS readings, data from sensors of the mobile device or thelike.

On Step 340, an involvement of the vehicle in an accident mayautomatically be determined, based on the data obtained from the mobiledevice.

As an example, the data obtained in Step 330 may comprise a recording ofa microphone of the mobile device. The recording may be analyzed toidentify an audio segment indicative of the accident, such as anaccident voice, a boom, a crash, a screaming, curses, or the like. Insome exemplary embodiments, the recordings may be compared withrecordings of other segments to identify an abnormal vocal behavior ofthe user, such as anger, excitement, or the like.

In some exemplary embodiments, a feature vector may be extracted fromthe data obtained from the mobile device. Each feature may berepresenting a piece of data from a different sensor of the mobiledevice, different types of data, or the like. The feature vector mayrepresent sensor readings in a sliding window with respect to some ofthe sensors, such as a last one minute of accelerometer readings, lasttwo minutes of accelerometer readings, or the like. Additionally oralternatively, the feature vector may represent a true/false indicationof an event, e.g., a loud sound was heard in the last 60 minutes, a hitwas detected in the pictures, or the like. In some exemplaryembodiments, a reading of the same sensor may be provided to differenttime windows which may or may not overlap. For example, accelerometerinformation may be provided for 60 windows, each of which of 10 seconds.

In some exemplary embodiments, the feature vector may be used with aclassifier implemented on a server or other device having a processor.The classifier may be used to predict a label for the feature vector,such as the label of accident. In some exemplary embodiments, theclassifier may be trained using training data. The classifier may be aglobal model based on behavior of many people or an individual model ofan individual person behavior. In some exemplary embodiments, cluster ofusers may be compiled, and for each cluster a model may be trained,thereby providing a model useful for “look-a-like” users that behave ina similar manner.

On Step 370, the accident may be reported to a third party, such asrescue services, insurance companies, family member, social networkcontacts, or the like. In some exemplary embodiments, the report maycomprise the time of the accident, information about the accident,pictures, or the like, based on the data obtained from the mobiledevice.

On Step 380, a calendar of the user may be updated based on theoccurrence of the accident. In some exemplary embodiments, the calendarmay be retained in the mobile device of the user. Additionally oralternatively, the calendar may be accessible from the mobile device. Asan example, the calendar may be updated to delay events that the usermay arrive late thereto, send cancelation messages to participants ofsuch events, or the like.

Referring now to FIG. 3B showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 331, application usage information may be obtained. In someexemplary embodiments, the data obtained from the mobile device maycomprise usage information of applications retained by the mobiledevice.

On Step 341, the usage information may be analyzed. In some exemplaryembodiments, the user may invoke specific applications, in case of beinginvolved in an accident, such as an insurance app, accident reportingapp, or the like. Additionally or alternatively, the user may browsespecific web pages.

Additionally or alternatively, some applications, background services,or the like, that are installed on the mobile device of the user detectthat the user is leaving the vehicle, that the user is walking, the useris injured, or the like.

On Step 342, an involvement of the vehicle in an accident mayautomatically be determined, based on the analysis of the applicationusage information.

Referring now to FIG. 3C showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 332, a call log of the mobile device may be obtained. In someexemplary embodiments, the data obtained from the mobile device maycomprise the call log of the mobile device. The call log may beretrieved directly from the mobile device, such as from the callhistory. Additionally or alternatively, the call log may be obtained byan application of the mobile device providing detailed data about phonecall details, such as time, duration, or the like, from a call recorderapplication, or the like.

On Step 333, a call pattern indicative of an occurrence of an accidentmay be detected in the call log.

In some exemplary embodiments, the call log may comprise an outgoingcall to a phone number of emergency services, an outgoing call to aphone number of an insurance company, or the like. Such an outgoing callmay be indicative of an occurrence of an accident, or may be whencombined with other data, being be indicative of an occurrence of anaccident.

Additionally or alternatively, the call pattern may be a pattern of aplurality of repeated calls to a phone number in a frequency higher thanan average call attempt frequency of the user. The plurality of repeatedcalls to the phone number may be in a frequency higher than an averagecall attempt frequency of the user to this specific phone number, thegeneral average call attempt frequency of the user to all phone number,or the like. A frequency in the call patter that may be indicative of anoccurrence of an accident, may be for example, twice the general averagecall attempt frequency of the user, three times the average call attemptfrequency of the user to this number, ten times the general average callattempt frequency of the user, or the like.

Additionally or alternatively, the call pattern may be a pattern of aplurality of outgoing calls and corresponding incoming calls to and fromfamily members of the user. Such calls may be shorter than usual asbeing performed to tell family members about the accident, informingthat the user is fine, or the like. The duration of the plurality ofoutgoing calls and corresponding incoming calls may be below an averagecall duration of the user.

In some exemplary embodiments, the call log may comprise a phone callthat was terminated abnormally. Such a phone call may be identifiedbased on having a duration below an average call duration of the userwith respect to the other participant of the call. Such a call may alsobe indicative of an occurrence of an accident.

On Step 343, an involvement of the vehicle in an accident mayautomatically be determined, based on the call pattern being indicativeof the occurrence of the vehicle.

Referring now to FIG. 3D showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 334, data from a motion sensor may be obtained. In someexemplary embodiments, the mobile device may comprise one or moresensors capable of sensing motion. As an example, the motion sensor maybe an accelerometer. The accelerometer may be configured to enable themobile device to derive different types of data about the user, such astracking steps, measure acceleration, switching apps from portrait tolandscape, showing current speed, or the like. As another example, themotion sensor may be a gyroscope. The gyroscope may be configured tohelp the accelerometer detecting the orientation of the computingdevice, adding another level of precision to turns performed by themobile device, or the like. As yet another example, the motion sensormay be a magnetometer. The magnetometer may be configured to measuremagnetic fields around the mobile device and can thus tell which way isnorth by varying its voltage output to the mobile device. Themagnetometer may be configured to operate in tandem with the data comingfrom the accelerometer or GPS unit to determine location and point ofthe user.

On Step 335, a driving pattern of the vehicle may be identified based onthe data obtained from the motion sensor. The driving pattern may beconsistent with a post-accident driving pattern. As an example, apost-accident driving pattern may comprise driving straight, thendriving back for few meters, and then turning to the side and stopping.As another example, a post-accident driving pattern may comprise drivingfast then suddenly stopping for a long time. Such movements of thevehicle may be determined based on the data obtained from the one ormore motion sensors, such as the accelerometer, the magnetometer, thegyroscope, a combination thereof, or the like. The motion sensors may beconfigured to sense gravity, linear acceleration, rotation vector,significant motion, step counter, or the like. The exact mobile devicemovement, such as tilt, shake, rotation, swing, or the like, may bedetermined. The movement may be a reflection of the physical environmentin which the mobile device is being held, such as moving with user whilethe user drives her vehicle, walking, or the like.

In some exemplary embodiments, the driving pattern may be determinedusing a combination of statistical characteristics of the accelerometerreadings, and other sensors readings. For example, a driving pattern maybe classified and distinguished from other activities by detectingconstant constraint vibrations occurring while driving compared toabrupt accelerations when walking or running or very low accelerationwhen the mobile device is not moving. Then, the direction of the drivingmay be determined based the change of the tilt rotation vector of themobile device.

Additionally or alternatively, a driving pattern of the vehicle in aroad segment that deviates from an average driving pattern of vehiclesin the road segment, may be indicative of a vehicle accident. Thedriving pattern may be detected automatically by analyzing in real timethe speed and or locations of vehicles on roads. An example, the drivingpattern may be that the vehicle had stopped in a road while othervehicles are driving, that the vehicle had stopped and there is trafficcongestion behind it, or the like.

On Step 343, an involvement of the vehicle in an accident mayautomatically be determined, based on the driving pattern beingindicative of the occurrence of the vehicle.

Additionally or alternatively, On Step 336, a mobility pattern or stateof the user of the mobile device may be identified based on the dataobtained from the motion sensor.

In some exemplary embodiments, a mobility state of the user of themobile device may be identified based on readings of the motion sensor.

On Step 343, an involvement of the vehicle in an accident mayautomatically be determined, based on the mobility state of the userbeing indicative of the occurrence of the vehicle. As an example, amobility state indicative of the user not riding the vehicle, may beindicative of the user getting off the vehicle as a result of anaccident.

Additionally or alternatively, a mobility pattern of the user may beindicative of the user being involved with the accident. The mobilitypattern may comprise an ordinal sequence of mobility states of the user.A mobility pattern indicative of an accident may comprise a firstmobility state indicative of the user riding a vehicle, a secondmobility state, immediately following the first mobility state,indicative of a sudden stop; and a third mobility state occurring afterthe second mobility state, indicative of a user walking. Such a patternmay be indicative of an accident resulting the sudden stop and forcingthe user to get off the vehicle.

Referring now to FIG. 3E showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 330, the data obtained from the mobile device may comprise datafrom a calendar of the user that is retained in the mobile device. Thedata may comprise schedule of the user, planned meetings, locations offuture plans, or the like.

On Step 337, a lack of matching between scheduled activities of theuser, as appearing in the calendar of the user, and actual activities ofthe user may be identified.

On Step 347, an involvement of the vehicle in an accident mayautomatically be determined, based on the identified lack of matching.It may be appreciated that in some exemplary embodiments, Step 347 maybe performed at least a predetermined time after the accident, such asafter one hour, after few hours, after a day, or the like. Thedetermination may be performed in retrospect to the accident.

Referring now to FIG. 3F showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 338, pictures may be obtained from the mobile device. In someexemplary embodiments, the data obtained from the mobile device maycomprise one or more pictures taken by the mobile device, such aspictures captured by a camera of the mobile device, pictures provided byan application of the mobile device, or the like.

On Step 339, the pictures may be analyzed. In some exemplaryembodiments, the pictures may be analyzed to identify a damaged vehicleor portion thereof, or any other elements indicative of an accident,such as damaged objects, injured people, or the like. Additionally oralternatively, the pictures may be analyzed to extract information aboutthe accident, vehicles involved in the accident, or the like. As anexample, a license plate of a vehicle may be automatically identified inthe one or more pictures. Based on the number in the license plate,vehicles involved in the accident may be identified. As another example,the location of the accident may be identified based on the one or morepictures.

On Step 348, an involvement of the vehicle in an accident mayautomatically be determined, based on the pictures. In some exemplaryembodiments, the determination may be performed based on identifying inthe one or more pictures a damaged vehicle or portion thereof.

On Step 349, an estimated severity level of the accident may beautomatically determined, based on the one or more pictures. In someexemplary embodiments, the severity level may be determined based on thedamage identified in the one or more pictures, based on injured peopleappearing in the one or more pictures, or the like.

In some exemplary embodiments, additional information about theaccident, such as the estimated severity level of the accident, involvedvehicles, pictures, or the like, may be provided in the report to thethird party performed on Step 370.

Referring now to FIG. 3G showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 350, an abnormally terminated phone call performed using themobile device may be identified. In some exemplary embodiments, theabnormally terminated call may be identified using a microphone of themobile device and based on a recording of a last period of the phonecall. Additionally or alternatively, the abnormally terminated call maybe identified based on identifying a phone call with a participanthaving a duration below an average call duration of the user withrespect to the participant.

On Step 370, a time of the accident may be determined based on a time ofa termination of the phone call.

Referring now to FIG. 3H showing a flowchart diagram of a method, inaccordance with some exemplary embodiments of the disclosed subjectmatter.

On Step 355, data from a computing device of the vehicle may beobtained. In some exemplary embodiments, the data obtained from thecomputing device of the vehicle may comprise one or more picturescaptured by a dash camera of the vehicle.

On Step 356, an involvement of the vehicle in an accident may be furtherdetermined, based on the data obtained from the computing device. As anexample, the involvement of the vehicle in the accident, and otherinformation related thereto, may be performed based on analysis of theone or more pictures captured by the dash camera of the vehicle.

On Step 390, the data obtained from the mobile device may be validatedbased on the data obtained from the vehicle's computing device.

Referring now to FIG. 4 showing a block diagram of an apparatus, inaccordance with some exemplary embodiments of the disclosed subjectmatter. An Apparatus 400 may be configured to support parallel userinteraction with a real world physical system and a digitalrepresentation thereof, in accordance with the disclosed subject matter.

In some exemplary embodiments, Apparatus 400 may comprise one or moreProcessor(s) 402. Processor 402 may be a Central Processing Unit (CPU),a microprocessor, an electronic circuit, an Integrated Circuit (IC) orthe like. Processor 402 may be utilized to perform computations requiredby Apparatus 400 or any of it subcomponents.

In some exemplary embodiments of the disclosed subject matter, Apparatus400 may comprise an Input/Output (I/O) module 405. I/O Module 405 may beutilized to provide an output to and receive input from other computingdevices, such as, for example Mobile Device 450, computing devices ofVehicles 475, mobile devices carried by users riding Vehicles 475, orthe like.

In some exemplary embodiments, Apparatus 400 may comprise Memory 407.Memory 407 may be a hard disk drive, a Flash disk, a Random AccessMemory (RAM), a memory chip, or the like. In some exemplary embodiments,Memory 407 may retain program code operative to cause Processor 402 toperform acts associated with any of the subcomponents of Apparatus 400.

In some exemplary embodiments, Apparatus 400 may be configured toanalyses the data obtained from the one or more Mobile Device 450, anddetermine, based thereon, that Vehicle 470 was involved in an accident.

In some exemplary embodiments, Apparatus 400 may be configured to obtainvia I/O Module 405 readings from one or more Mobile Device(s) 450.Mobile Device 450(s) may be carried by one or more User(s) 460. One ormore User(s) 460 may be riding in a Vehicle 470, such as a driver,passengers, or the like. Mobile Device 450 may not be affixed to Vehicle470, rather may be temporary associated therewith.

In some exemplary embodiments, some of the readings obtained from MobileDevice 450 may be accelerometer readings of An Accelerometer 451 ofMobile Device 450. Mobility Analysis Module 410 may be configured todetermine, based on the readings obtained from Mobile Device 450, thatUser 460 is riding in Vehicle 470. Mobility Analysis Module 410 may beconfigured to analyze the readings obtained from Mobile Device 450, suchas Accelerometer 451 readings, to determine mobility state of User 460.The mobility state may be standing, walking, running, riding a bicycle,riding a car in a slow road, riding a car in a highway, or the like.Apparatus 400 may be configured to determine that User 460 is ridingVehicle 470 based on the mobility state of User 460. It may beappreciated that the results may be validated or supported based on datafrom other sensors of Mobile Device 450 or Vehicle 470.

In some exemplary embodiments, Mobility Analysis Module 410 may beconfigured to identify, based on the readings of Accelerometer 451, adriving pattern of Vehicle 470 that is consistent with a post-accidentdriving pattern, such as a sudden stop after a fast driving, or thelike. Mobility Analysis Module 410 may utilize additional data obtainedfrom other sensors od Mobile Device 450 that are capable of sensingmotion, such as Barometer 452, Speedometer 453, or the like, to identifythe driving pattern. Mobility Analysis Module 410 may be configured toanalyze such data to determine the speed, direction, acceleration, orthe like, of Vehicle 470. Accident Detector 440 may be configured todetermine that Vehicle 470 was involved in an accident, based on thedriving pattern of Vehicle 470 being consistent with a post-accidentdriving pattern. Additionally or alternatively, Mobility Analysis Module410 may be configured to identify, based on the data obtained from themotion sensors, a driving pattern of Vehicle 470 in a road segment thatdeviates from an average driving pattern of other vehicles, such asVehicle 475, in the road segment. As an example, Vehicle 470 may stop inthe middle of the road, while the other vehicles continue to drive,Vehicle 470 may be stopping while the other vehicles slowing down whenpassing besides Vehicle 470 an d returning to their previous speed whenbeing after Vehicle 470. Accident Detector 440 may be configured todetermine that Vehicle 470 was involved in an accident, based on thedriving pattern of Vehicle 470 being different than the driving patternof the other vehicles in the same road segment.

Additionally or alternatively, Mobility Analysis Module 410 may beconfigured to identify, based on the data obtained from the motionsensors, a mobility state of User 460. Accident Detector 440 may beconfigured to determine that Vehicle 470 was involved in an accident,based on the mobility state of User 460 being indicative of User 460 notriding Vehicle 470. Determining that User 460 not riding Vehicle 470,after already determining that User 460 was riding Vehicle 470, and theVehicle 470 is in a middle of a planned drive, or in the middle of aroad, or the like, may be an indication that User 460 had to get offVehicle 470 because of an emergency event, such as an accident.

Additionally or alternatively, Mobility Analysis Module 410 may beconfigured to identify, based on the data obtained from the motionsensors, a mobility pattern of User 460 that is indicative of User 460being involved with the accident. The mobility pattern may comprise anordinal sequence of mobility states of User 460, indicative of User 460being involved with the accident. As an example, the mobility patternmay comprise a sequence of a first mobility state indicative of User 460riding a vehicle, a second mobility state, immediately following thefirst mobility state, indicative of a sudden stop; and a third mobilitystate occurring after the second mobility state, indicative of User 460walking. Such a mobility pattern may be indicative of Vehicle 470 beinginvolved in an accident, as User 460 riding Vehicle 470, may be in astate of a sudden stop when the accident occurs, and may get off Vehicle470 and walk to check what happened to Vehicle 470.

In some exemplary embodiments, Accident Detector 440 may be configuredto determine that Vehicle 470 was involved in an accident at least apredetermined time after the accident, such as after an hour, after fewhours, at the end of the day, the next day, or the like. AccidentDetector 440 may be configured to determine that Vehicle 470 wasinvolved in an accident based on a lack of matching between scheduledactivities of User 460, as appearing in a Calendar 459 of User 460 thatis retained in Mobile Device 450, and actual activities of User 460. Theactual activities of User 460 may be determined based on data obtainedfrom Apps installed on Mobile Device 450, based on data obtained fromother sensors of Mobile Device 450, based on location data obtained froma GPS Sensor 457 of Device 450, or the like.

In some exemplary embodiments, the data obtained from Mobile Device 450may comprise a call log of Mobile Device 450. Call Log Analysis Module415 may be configured to analyze the call log of Mobile Device 450 todetect a call pattern in a call log indicative of an occurrence of anaccident. The call pattern may be an outgoing call to a phone number ofemergency services, an outgoing call to a phone number of an insurancecompany, a plurality of repeated calls to a phone number in a frequencyhigher than an average call attempt frequency of the user, a pluralityof outgoing calls and corresponding incoming calls to and from familymembers of the user, with a duration of the plurality of outgoing callsand corresponding incoming calls being below an average call duration ofthe user, or the like. Accident Detector 440 may be configured todetermine that Vehicle 470 was involved in an accident based on the callpattern being indicative of an occurrence of an accident.

Additionally or alternatively, Call Log Analysis Module 415 may beconfigured to identify, in the call log of Mobile Device 450, a phonecall performed using the mobile device that was terminated abnormally.As an example, Call Log Analysis Module 415 may be configured toidentify a phone call with a participant having a duration below anaverage call duration of the user with respect to the participant. Asanother example, Call Log Analysis Module 415 may be configured toanalyze some of the last phone calls, or recordings thereof, todetermine that a phone call was terminated in a different manner thanusual, that a phone call has been terminated abnormally and there was nocomplementary phone call thereto, that a phone call has been terminatedabnormally and there was a complementary phone call thereto (e.g., fortelling about the accident), or the like. Additionally or alternatively,Call Log Analysis Module 415 may be configured to analyze a recording ofa last period of a last phone call to determine that such a phone callwas terminated abnormally. The recording may be obtained from aMicrophone 454 of Mobile Device 450 via I/O Module 405. AccidentDetector 440 may be configured to determine a time of the accident basedon a time of a termination of the phone call. In some cases, the precisetiming may not be known. GPS and location information obtained fromMobile Device 450 may be used to determine when User 460 was located ator near the place of the accident, and anomaly detection may be used toidentify the timeframe in which User 460 acted abnormally to identifywhen exactly the accident occurred.

In some exemplary embodiments, pictures taken by Mobile Device 450, suchas by Camera 456, by Apps 458, or the like, may be obtained via I/OModule 405. Image Analysis Module 420 may be configured to analyze thepictures in order to identify elements or motifs indicative of anaccident, such as a damaged vehicle, a portion of a damaged vehicle,injured people, crash, or the like. Additionally or alternatively,Accident Detector 440 may be configured to determine that Vehicle 470was involved in an accident based on pictures being indicative thereof.Additionally or alternatively, Image Analysis Module 420 may beconfigured to analyze the pictures in order to extract additionalinformation about the accident. As an example, Image Analysis Module 420may be configured to identify a license plate of a vehicle in thepictures, other identifiers of vehicles, or the like. Accident Detector440 may be configured to determine, based on analysis of the picturesperformed by Image Analysis Module 420, an estimated severity level ofthe accident, information about vehicles involved in the accident, orthe like.

In some exemplary embodiments, the data obtained from Mobile Device 450may comprise recordings from a Microphone 454 of Mobile Device 450.Audio Analysis Module 425 may be configured to analyze the recordings inorder to identify an audio segment indicative of an accident, such as avoice of a crash, a screaming, or the like. Accident Detector 440 may beconfigured to determine that Vehicle 470 was involved in an accidentbased on the recordings comprising an audio segment indicative of anaccident.

In some exemplary embodiments, the data obtained from Mobile Device 450may comprise a usage information of a plurality of applications ofMobile Device 450, such as Apps 458. App Usage Analysis Module 430 maybe configured to analyze the usage information of the plurality ofapplications to determine an indication of an accident, such as using aninsurance application, using an auto filling car accident reportapplication, or the like. Accident Detector 440 may be configured todetermine that Vehicle 470 was involved in an accident based on theusage information being indicative of an accident.

In some exemplary embodiments, Apparatus 400 may be configured to obtainvia I/O Module 405 data from a computing device of Vehicle 470. Thecomputing device may be capable of sensing the environment of Vehicle470, based on a variety of sensors of Vehicle 470, such as radars,computer vision, Lidar, sonar, GPS, odometer, inertial measurementunits, or the like. In some exemplary embodiments, the data may compriseone or more pictures captured by a dash camera of Vehicle 470. ImageAnalysis Module may be configured to analyze the one or more pictures inorder to determine that Vehicle 470 was involved in an accident, toextract additional information about the accident, or the like.

In some exemplary embodiments, Accident Detector 440 may be configuredto extract a feature vector from the data obtained from Mobile Device450. The feature vector may be extracted from the data obtained from thevarious sensors and sources of Mobile Device 450, computing devices ofVehicle 470 or Vehicles 475, other mobile devices, or the like. Thefeature vector may represent sensor readings in a sliding window withrespect to some of the sensors, such as readings of last one minute ofAccelerometer 451, pictures captured in the last 5 minutes, or the like.Additionally or alternatively, the feature vector may represent atrue/false indication of an event, e.g., a loud sound was heard in thelast 60 minutes, a picture of a damaged vehicle was captured in the last10 minutes, or the like. In some exemplary embodiments, a reading of thesame sensor may be provided to different time windows which may or maynot overlap. For example, accelerometer information may be provided for10 windows, 20 windows, 60 windows, or the like, each of which of 10seconds, 20 seconds, or the like.

In some exemplary embodiments, the feature vector may be used with aClassifier 435. Classifier 435 may be utilized to predict a label forthe feature vector, such as a label of accident occurred or no accidentoccurred, a label representing the severity level of the accident, orthe like. In some exemplary embodiments, Classifier 435 may be trainedusing training data. Classifier 435 may be a global model based onbehavior of many people, an individual model of an individual personbehavior representing User 4650, or the like. In some exemplaryembodiments, a cluster of users may be compiled, and for each cluster amodel may be trained, thereby providing a model useful for “look-a-like”users that behave in a similar manner.

In some exemplary embodiments, The training data may be obtained fromDatabase 480 and may comprise pairs of data pieces and labels thereof,pairs of features and labels thereof, pairs of vector values and labelsthereof, or the like. The training data may be obtained using data onaccidents from third party sources, such as incidents informationservices like Waze™, Google™ maps, or the like. Additionally oralternatively, The training data may be obtained using information fromservice providers such as insurance companies, car manufacturers callcenters, or the like. The data may be used to obtain a label andcorrelate the label with a feature vector that is obtained from thethird party source or obtained using an agent installed on Apparatus400. For example, an agent, e.g. Accident Detector 440, may monitorbehavior of User 460 and the information about the accident may beobtained from the insurance company of User 460. Using such information,the behavior of User 460 at the time of the accident may be obtainedfrom the agent's log.

In some exemplary embodiments, Apparats 400 may be configured to issue,via I/O module, a report of the accident to a third party, such as toemergency services, insurance agent, or the like. Additionally oralternatively, the report may be provided to instances of Mobile Device450, such as to a message application sending the report to contacts ofMobile Device 450, to Calendar 459, to social network Apps 458, or thelike.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method comprising: obtaining readings from amobile device of a user, wherein the mobile device is being carried bythe user, wherein the user is riding in a vehicle, wherein the mobiledevice is not affixed to the vehicle; determining, based on the readingsobtained from the mobile device, that the user is riding in the vehicle;obtaining data from the mobile device of the user; and determiningautomatically, based on the data obtained from the mobile device, thatthe vehicle was involved in an accident.
 2. The method of claim 1,wherein said obtaining the data comprises obtaining a call log of themobile device; wherein said determining automatically that the vehiclewas involved in an accident comprises detecting a call pattern in thecall log indicative of an occurrence of an accident.
 3. The method ofclaim 2, wherein the call pattern is selected from the group consistingof: an outgoing call to a phone number of emergency services; anoutgoing call to a phone number of an insurance company; a plurality ofrepeated calls to a phone number in a frequency higher than an averagecall attempt frequency of the user; and a plurality of outgoing callsand corresponding incoming calls to and from family members of the user,wherein a duration of the plurality of outgoing calls and correspondingincoming calls is below an average call duration of the user.
 4. Themethod of claim 1 further comprises identifying a phone call performedusing the mobile device that was terminated abnormally, and determininga time of the accident based on a time of a termination of the phonecall.
 5. The method of claim 4, wherein said identifying the phone callis performed using a microphone of the mobile device and based on arecording of a last period of the phone call.
 6. The method of claim 4,wherein said identifying the phone call is performed based on a call logof the mobile device, wherein said identifying comprises identifying aphone call with a participant having a duration below an average callduration of the user with respect to the participant.
 7. The method ofclaim 1, wherein the mobile device comprising a sensor capable ofsensing motion; wherein said determining automatically that the vehiclewas involved in the accident comprises identifying a driving pattern ofthe vehicle that is consistent with a post-accident driving pattern. 8.The method of claim 1, wherein the mobile device comprising a sensorcapable of sensing motion; wherein said determining automatically thatthe vehicle was involved in the accident comprises identifying a drivingpattern of the vehicle in a road segment that deviates from an averagedriving pattern of vehicles in the road segment.
 9. The method of claim1, wherein the mobile device comprising a sensor capable of sensingmotion; wherein said determining automatically that the vehicle wasinvolved in an accident comprises: identifying a mobility state of theuser, based on readings of the sensor, indicative of the user not ridingthe vehicle.
 10. The method of claim 1, wherein the mobile devicecomprising a sensor capable of sensing motion; wherein said determiningautomatically that the vehicle was involved in an accident comprises:identifying a mobility pattern of the user comprising an ordinalsequence of mobility states of the user, wherein the mobility pattern isindicative of the user being involved with the accident.
 11. The methodof claim 10, wherein the mobility pattern comprises at least a sequenceof a first mobility state indicative of the user riding a vehicle, asecond mobility state, immediately following the first mobility state,indicative of a sudden stop; and a third mobility state occurring afterthe second mobility state, indicative of a user walking.
 12. The methodof claim 1, wherein said determining automatically that the vehicle wasinvolved in an accident is performed at least a predetermined time afterthe accident, wherein said determining is performed based on a lack ofmatching between scheduled activities of the user, as appearing in acalendar of the user that is retained in the mobile device, and actualactivities of the user.
 13. The method of claim 1, wherein the dataobtained from the mobile device is one or more pictures taken by themobile device, wherein said determining automatically that the vehiclewas involved in an accident is performed based on identifying in the oneor more pictures a damaged vehicle or portion thereof.
 14. The method ofclaim 13 further comprises automatically determining, based on the oneor more pictures, an estimated severity level of the accident.
 15. Themethod of claim 13 further comprises automatically identifying in theone or more pictures, a license plate of a vehicle, and determining oneor more involved vehicles in the accident.
 16. The method of claim 1,wherein the mobile device comprising a microphone, wherein saidobtaining data comprises obtaining a recording of the microphone,wherein said determining automatically that the vehicle was involved inan accident comprises analyzing the recording to identify an audiosegment indicative of the accident.
 17. The method of claim 1, whereinthe mobile device retaining a plurality of applications, wherein saidobtaining data comprises obtaining a usage information of the pluralityof applications, wherein said determining automatically that the vehiclewas involved in an accident comprises analyzing the usage information ofthe plurality of applications.
 18. The method of claim 1 furthercomprises, in response to said determining automatically that thevehicle was involved in an accident, automatically reporting theaccident to a third party.
 19. The method of claim 1 further comprises,in response to said determining automatically that the vehicle wasinvolved in an accident, automatically updating a calendar of the user,wherein the calendar is retained in the mobile device.
 20. The method ofclaim 1, wherein said determining automatically that the vehicle wasinvolved in the accident comprises extracting a feature vector from thedata, wherein the feature vector is associated with a sliding windowwith respect to a sensor of the mobile device.
 21. The method of claim1, wherein the mobile device comprising an accelerometer, wherein saidobtaining the readings comprises obtaining accelerometer readings fromthe accelerometer, wherein said determining that the user is riding inthe vehicle comprises analyzing the accelerometer readings of the mobiledevice to determine mobility state of the user.
 22. The method of claim1, further comprises: obtaining data from a computing device of thevehicle; wherein said determining automatically that the vehicle wasinvolved in an accident, is further determined based on the dataobtained from the computing device.
 23. The method of claim 22, furthercomprises: performing an authentication of said determiningautomatically that the vehicle was involved in an accident, based on thedata obtained from the computing device.
 24. The method of claim 22,wherein the data obtained from the computing device of the vehiclecomprises one or more pictures captured by a dash camera of the vehicle;wherein said determining automatically that the vehicle was involved inan accident is performed based on analysis of the one or more pictures.25. A non-transitory computer readable medium retaining programinstructions, which program instructions when read by a processor, causethe processor to perform: obtaining readings from a mobile device of auser, wherein the mobile device is being carried by the user, whereinthe user is riding in a vehicle, wherein the mobile device is notaffixed to the vehicle; determining, based on the readings obtained fromthe mobile device, that the user is riding in the vehicle; obtainingdata from the mobile device of the user; and determining automatically,based on the data obtained from the mobile device, that the vehicle wasinvolved in an accident.
 26. An apparatus comprising: a processor and amemory, wherein the memory retaining said non-transitory computerreadable medium of claim 25.